YOLOv8: How to Train for Object Detection on a Custom Dataset

YOLOv8 is the latest installment of the highly influential YOLO (You Only Look Once) architecture. YOLOv8 was developed by Ultralytics, a team known for its work on YOLOv3 and YOLOv5. Following the trend set by  YOLOv6 and YOLOv7, we have at our disposal object detection, but also instance segmentation, and image classification. The model itself is created in PyTorch and runs on both the CPU and GPU. As with YOLOv5, we also have a number of various exports such as or CoreML. In this video, I’ll take you through a step-by-step tutorial on Google Colab, and show you how to train your own YOLOv8 object detection model. Chapters: 0:00 Introduction 0:51 Overview 3:09 Setting up the Python environment 5:36 New API: CLI vs. Python SDK 8:51 Prepare the YOLOv8 object detection dataset 12:29 Train YOLOv8 model on custom dataset 13:54 YOLOv8 model evaluation 16:47 YOLOv8 model inference on images and videos 18:44 YOLOv8 model deployment and inference v